{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.rcParams['font.family'] = ['Arial Unicode MS'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False #用来正常显示负号\n",
    "sns.set_style('whitegrid',{'font.sans-serif':['Arial Unicode MS','Arial']})\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "      <th>同比增长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>491</td>\n",
       "      <td>307</td>\n",
       "      <td>-0.374745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>重庆</td>\n",
       "      <td>648</td>\n",
       "      <td>307</td>\n",
       "      <td>-0.526235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>西安</td>\n",
       "      <td>352</td>\n",
       "      <td>933</td>\n",
       "      <td>1.650568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>郑州</td>\n",
       "      <td>596</td>\n",
       "      <td>523</td>\n",
       "      <td>-0.122483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>苏州</td>\n",
       "      <td>918</td>\n",
       "      <td>275</td>\n",
       "      <td>-0.700436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>西宁</td>\n",
       "      <td>842</td>\n",
       "      <td>252</td>\n",
       "      <td>-0.700713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>济南</td>\n",
       "      <td>502</td>\n",
       "      <td>369</td>\n",
       "      <td>-0.264940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>天津</td>\n",
       "      <td>62</td>\n",
       "      <td>903</td>\n",
       "      <td>13.564516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>广州</td>\n",
       "      <td>141</td>\n",
       "      <td>40</td>\n",
       "      <td>-0.716312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>深圳</td>\n",
       "      <td>338</td>\n",
       "      <td>303</td>\n",
       "      <td>-0.103550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市  2017年6月  2018年6月       同比增长\n",
       "0  北京      491      307  -0.374745\n",
       "1  重庆      648      307  -0.526235\n",
       "2  西安      352      933   1.650568\n",
       "3  郑州      596      523  -0.122483\n",
       "4  苏州      918      275  -0.700436\n",
       "5  西宁      842      252  -0.700713\n",
       "6  济南      502      369  -0.264940\n",
       "7  天津       62      903  13.564516\n",
       "8  广州      141       40  -0.716312\n",
       "9  深圳      338      303  -0.103550"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_excel('iphoneX_201706.xlsx')\n",
    "df2 = pd.read_excel('iphoneX_201806.xlsx')\n",
    "\n",
    "df = pd.concat((df1, df2.drop(['城市'], axis=1)), axis=1)\n",
    "df['同比增长'] = (df['2018年6月']-df['2017年6月'])/df['2017年6月']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>指标：销量  \n",
    "维度：地区、时间</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "      <th>同比增长</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>489.000000</td>\n",
       "      <td>421.200000</td>\n",
       "      <td>1.170567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>276.998997</td>\n",
       "      <td>287.233857</td>\n",
       "      <td>4.409915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>-0.716312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>341.500000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>-0.656885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>496.500000</td>\n",
       "      <td>307.000000</td>\n",
       "      <td>-0.319843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>635.000000</td>\n",
       "      <td>484.500000</td>\n",
       "      <td>-0.108284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>933.000000</td>\n",
       "      <td>13.564516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2017年6月     2018年6月       同比增长\n",
       "count   10.000000   10.000000  10.000000\n",
       "mean   489.000000  421.200000   1.170567\n",
       "std    276.998997  287.233857   4.409915\n",
       "min     62.000000   40.000000  -0.716312\n",
       "25%    341.500000  282.000000  -0.656885\n",
       "50%    496.500000  307.000000  -0.319843\n",
       "75%    635.000000  484.500000  -0.108284\n",
       "max    918.000000  933.000000  13.564516"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看描述性统计\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'2018年6月概率分布')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10d413c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10d90c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 产看数据概率分布\n",
    "plt.figure()\n",
    "sns.distplot(df['2017年6月'])\n",
    "plt.title('2017年6月概率分布')\n",
    "plt.figure()\n",
    "sns.distplot(df['2018年6月'])\n",
    "plt.title('2018年6月概率分布')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>1、可以看到，2017年6月数据概率分布基本呈正态分布，2018年6月数据分布基本呈双峰分布</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a10e395f8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10e21a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成新列销售时间\n",
    "df_1 = pd.DataFrame()\n",
    "df_1['城市'] = df1['城市']\n",
    "df_1['销售数量'] = df1['2017年6月']\n",
    "df_1['销售时间'] = '2017年6月'\n",
    "\n",
    "df_2 = pd.DataFrame()\n",
    "df_2['城市'] = df2['城市']\n",
    "df_2['销售数量'] = df2['2018年6月']\n",
    "df_2['销售时间'] = '2018年6月'\n",
    "\n",
    "df_3 = pd.concat((df_1, df_2), axis=0)\n",
    "\n",
    "# 按时间维度绘制柱形图\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.barplot(x='城市', y='销售数量', hue='销售时间', data = df_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>2、从时间维度上来看除了西安，天津之外，iphoneX的总体销量呈下降趋势</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'2018年6月iphoneX销售情况')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a141a4a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a144cd6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 同一时间维度对比销量情况,按销量排序\n",
    "df_1 = df_1.sort_values(by='销售数量',ascending=False).reset_index(drop=True)\n",
    "df_2 = df_2.sort_values(by='销售数量',ascending=False).reset_index(drop=True)\n",
    "plt.figure(figsize=(12,6))\n",
    "x = df_1['城市']\n",
    "y = df_1['销售数量']\n",
    "plt.xticks(np.arange(len(x)), x)\n",
    "plt.bar(np.arange(len(x)),y)\n",
    "plt.ylabel('销售数量')\n",
    "plt.title('2017年6月iphoneX销售情况')\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "x = df_2['城市']\n",
    "y = df_2['销售数量']\n",
    "plt.xticks(np.arange(len(x)), x)\n",
    "plt.bar(np.arange(len(x)),y)\n",
    "plt.ylabel('销售数量')\n",
    "plt.title('2018年6月iphoneX销售情况')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>3、可以看到销售排名发生了变化  \n",
    "    &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2017年6月苏州、西宁销量最好  \n",
    "&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2018年6月天津、西安销量最好</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 10 artists>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a142e19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dff = df.sort_values(by='同比增长',ascending=False).reset_index(drop=True)\n",
    "plt.figure(figsize=(12,6))\n",
    "x = dff['城市']\n",
    "y = dff['同比增长']\n",
    "plt.xticks(np.arange(len(x)), x)\n",
    "plt.bar(left=np.arange(len(x)), height=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>4、天津的销量同比增长远远高于西安，深圳最为稳定</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017年总销量: 4890\n",
      "2018年总销量: 4212\n"
     ]
    }
   ],
   "source": [
    "# 2017年总销量\n",
    "print('2017年总销量:', df['2017年6月'].sum())\n",
    "# 2018年总销量\n",
    "print('2018年总销量:', df['2018年6月'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>5、从总体销量情况来看，销量呈下降趋势</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一线城市</th>\n",
       "      <td>970</td>\n",
       "      <td>650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二三线城市</th>\n",
       "      <td>3920</td>\n",
       "      <td>3562</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2017年6月  2018年6月\n",
       "城市                     \n",
       "一线城市       970      650\n",
       "二三线城市     3920     3562"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在时间维度上卷，将城市划分为一线城市、二三线城市（因为这里只有西宁一个三线城市，故不做单独处理）\n",
    "df_4 = df\n",
    "df_4['城市'].replace(['北京', '广州', '深圳'], ['一线城市', '一线城市', '一线城市'], inplace=True)\n",
    "df_4['城市'].replace(['重庆', '西宁', '西安', '郑州', '苏州', '济南', '天津'], \n",
    "                   ['二三线城市', '二三线城市', '二三线城市', '二三线城市', '二三线城市', '二三线城市', '二三线城市'], inplace=True)\n",
    "df_4.drop(['同比增长'], axis=1, inplace=True)\n",
    "# 求和\n",
    "df_5 = df_4.groupby(['城市']).sum()\n",
    "df_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一线城市</th>\n",
       "      <td>323.333333</td>\n",
       "      <td>216.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二三线城市</th>\n",
       "      <td>560.000000</td>\n",
       "      <td>508.857143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          2017年6月     2018年6月\n",
       "城市                           \n",
       "一线城市   323.333333  216.666667\n",
       "二三线城市  560.000000  508.857143"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 均值\n",
    "df_6 = df_4.groupby(['城市']).mean()\n",
    "df_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>城市</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一线城市</th>\n",
       "      <td>338</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>二三线城市</th>\n",
       "      <td>596</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2017年6月  2018年6月\n",
       "城市                     \n",
       "一线城市       338      303\n",
       "二三线城市      596      369"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 中位数\n",
    "df_7 = df_4.groupby(['城市']).median()\n",
    "df_7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>648</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>352</td>\n",
       "      <td>933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>596</td>\n",
       "      <td>523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>918</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>842</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>502</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>二三线城市</td>\n",
       "      <td>62</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      城市  2017年6月  2018年6月\n",
       "1  二三线城市      648      307\n",
       "2  二三线城市      352      933\n",
       "3  二三线城市      596      523\n",
       "4  二三线城市      918      275\n",
       "5  二三线城市      842      252\n",
       "6  二三线城市      502      369\n",
       "7  二三线城市       62      903"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14808c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_8 = df_4[df_4['城市'] == '二三线城市']\n",
    "fig = plt.figure(figsize=(15, 6))\n",
    "fig.add_subplot(121)\n",
    "sns.distplot(df_8['2017年6月'])\n",
    "fig.add_subplot(122)\n",
    "sns.distplot(df_8['2018年6月'])\n",
    "df_8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>2017年6月</th>\n",
       "      <th>2018年6月</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一线城市</td>\n",
       "      <td>491</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>一线城市</td>\n",
       "      <td>141</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>一线城市</td>\n",
       "      <td>338</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     城市  2017年6月  2018年6月\n",
       "0  一线城市      491      307\n",
       "8  一线城市      141       40\n",
       "9  一线城市      338      303"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a148082b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_9 = df_4[df_4['城市'] != '二三线城市']\n",
    "fig = plt.figure(figsize=(15, 6))\n",
    "fig.add_subplot(121)\n",
    "sns.distplot(df_9['2017年6月'])\n",
    "fig.add_subplot(122)\n",
    "sns.distplot(df_9['2018年6月'])\n",
    "df_9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=blue>6、一线城市的总体销售情况大幅度下降，而二线城市只有小幅下降  \n",
    "7、从购买力上来看，二三线城市是主力军，而一线城市的购买人数并不是很多  \n",
    "8、一线城市的总体购买情况波动不大，而不同的二线城市却出现了很大的波动（中位数发生变化，概率分布由右偏变为左偏）</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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